修改为东南天坐标系

backend_service/venv/lib/python3.13/site-packages/unstructured/embed/huggingface.py

@@ -0,0 +1,67 @@
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, List, Optional
+
+import numpy as np
+from pydantic import Field
+
+from unstructured.documents.elements import (
+    Element,
+)
+from unstructured.embed.interfaces import BaseEmbeddingEncoder, EmbeddingConfig
+from unstructured.utils import requires_dependencies
+
+if TYPE_CHECKING:
+    from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+
+
+class HuggingFaceEmbeddingConfig(EmbeddingConfig):
+    model_name: Optional[str] = Field(default="sentence-transformers/all-MiniLM-L6-v2")
+    model_kwargs: Optional[dict] = Field(default_factory=lambda: {"device": "cpu"})
+    encode_kwargs: Optional[dict] = Field(default_factory=lambda: {"normalize_embeddings": False})
+    cache_folder: Optional[dict] = Field(default=None)
+
+    @requires_dependencies(
+        ["langchain_huggingface"],
+        extras="embed-huggingface",
+    )
+    def get_client(self) -> "HuggingFaceEmbeddings":
+        """Creates a langchain Huggingface python client to embed elements."""
+        from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+
+        client = HuggingFaceEmbeddings(**self.dict())
+        return client
+
+
+@dataclass
+class HuggingFaceEmbeddingEncoder(BaseEmbeddingEncoder):
+    config: HuggingFaceEmbeddingConfig
+
+    def get_exemplary_embedding(self) -> List[float]:
+        return self.embed_query(query="Q")
+
+    def num_of_dimensions(self):
+        exemplary_embedding = self.get_exemplary_embedding()
+        return np.shape(exemplary_embedding)
+
+    def is_unit_vector(self):
+        exemplary_embedding = self.get_exemplary_embedding()
+        return np.isclose(np.linalg.norm(exemplary_embedding), 1.0)
+
+    def embed_query(self, query):
+        client = self.config.get_client()
+        return client.embed_query(str(query))
+
+    def embed_documents(self, elements: List[Element]) -> List[Element]:
+        client = self.config.get_client()
+        embeddings = client.embed_documents([str(e) for e in elements])
+        elements_with_embeddings = self._add_embeddings_to_elements(elements, embeddings)
+        return elements_with_embeddings
+
+    def _add_embeddings_to_elements(self, elements, embeddings) -> List[Element]:
+        assert len(elements) == len(embeddings)
+        elements_w_embedding = []
+
+        for i, element in enumerate(elements):
+            element.embeddings = embeddings[i]
+            elements_w_embedding.append(element)
+        return elements